The advent of digital multimedia such as digital images, speech/audio, graphics, and video have significantly improved various applications as well as opened up brand new applications due to relative ease by which it has enabled reliable storage, communication, transmission, and, search and access of content. Overall, the applications of digital multimedia have been many, encompassing a wide spectrum including entertainment, information, medicine, and security, and have benefited the society in numerous ways. Multimedia as captured by sensors such as cameras and microphones is often analog, and the process of digitization in the form of Pulse Coded Modulation (PCM) renders it digital. However, just after digitization, the amount of resulting data can be quite significant as is necessary to re-create the analog representation needed by speakers and/or TV display. Thus, efficient communication, storage or transmission of the large volume of digital multimedia content requires its compression from raw PCM form to a compressed representation. Thus, many techniques for compression of multimedia have been invented. Over the years, video compression techniques have grown very sophisticated to the point that they can often achieve high compression factors between 10 and 100 while retaining high psycho-visual quality, often similar to uncompressed digital video.
While tremendous progress has been made to date in the art and science of video compression (as exhibited by the plethora of standards bodies driven video coding standards such as MPEG-1, MPEG-2, H.263, MPEG-4 part2, MPEG-4 AVC/H.264, MPEG-4 SVC and MVC, as well as industry driven proprietary standards such as Windows Media Video, RealVideo, On2 VP, and the like), the ever increasing appetite of consumers for even higher quality, higher definition, and now 3D (stereo) video, available for access whenever, wherever, has necessitated delivery via various means such as DVD/BD, over the air broadcast, cable/satellite, wired and mobile networks, to a range of client devices such as PCs/laptops, TVs, set top boxes, gaming consoles, portable media players/devices, smartphones, and wearable computing devices, fueling the desire for even higher levels of video compression. In the standards-body-driven standards, this is evidenced by the recently started effort by ISO MPEG in High Efficiency Video coding which is expected to combine new technology contributions and technology from a number of years of exploratory work on H.265 video compression by ITU-T standards committee.
All aforementioned standards employ a general interframe predictive coding framework that involves reducing temporal redundancy by compensating for motion between frames of video. The basic concept is to remove the temporal dependencies between neighboring pictures by using block matching method. At the outset of an encoding process, each frame of the unencoded video sequence is grouped into one of three categories: I-type frames, P-type frames, and B-type frames. I-type frames are intra-coded. That is, only information from the frame itself is used to encode the picture and no inter-frame motion compensation techniques are used (although intra-frame motion compensation techniques may be applied).
The other two types of frames, P-type and B-type, are encoded using inter-frame motion compensation techniques. The difference between P-picture and B-picture is the temporal direction of the reference pictures used for motion compensation. P-type pictures utilize information from previous pictures in display order, whereas B-type pictures may utilize information from both previous and future pictures in display order.
For P-type and B-type frames, each frame is then divided into blocks of pixels, represented by coefficients of each pixel's luma and chrominance components, and one or more motion vectors are obtained for each block (because B-type pictures may utilize information from both a future and a past coded frame, two motion vectors may be encoded for each block). A motion vector (MV) represents the spatial displacement from the position of the current block to the position of a similar block in another, previously encoded frame (which may be a past or future frame in display order), respectively referred to as a reference block and a reference frame. The difference between the reference block and the current block is calculated to generate a residual (also referred to as a “residual signal”). Therefore, for each block of an inter-coded frame, only the residuals and motion vectors need to be encoded rather than the entire contents of the block. By removing this kind of temporal redundancy between frames of a video sequence, the video sequence can be compressed.
To further compress the video data, after inter or intra frame prediction techniques have been applied, the coefficients of the residual signal are often transformed from the spatial domain to the frequency domain (e.g. using a discrete cosine transform (“DCT”) or a discrete sine transform (“DST”)). For naturally occurring images, such as the type of images that typically make up human perceptible video sequences, low-frequency energy is always stronger than high-frequency energy. Residual signals in the frequency domain therefore get better energy compaction than they would in spatial domain. After forward transform, the coefficients and motion vectors may be quantized and entropy encoded.
On the decoder side, inversed quantization and inversed transforms are applied to recover the spatial residual signal. These are typical transform/quantization process in all video compression standards. A reverse prediction process may then be performed in order to generate a recreated version of the original unencoded video sequence.
In past standards, the blocks used in coding were generally sixteen by sixteen pixels (referred to as macroblocks in many video coding standards). However, since the development of these standards, frame sizes have grown larger and many devices have gained the capability to display higher than “high definition” (or “HD”) frame sizes, such as 2048×1530 pixels. Thus it may be desirable to have larger blocks to efficiently encode the motion vectors for these frame size, e.g. 64×64 pixels. However, because of the corresponding increases in resolution, it also may be desirable to be able to perform motion prediction and transformation on a relatively small scale, e.g. 4×4 pixels.
As the resolution of motion prediction increases, the amount of bandwidth required to encode and transmit motion vectors increases, both per frame and accordingly across entire video sequences.